Agentic AI for Finance & Procurement 

AI that doesn't just assist finance teams — it helps perform the work 

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Kanbina brings together Agentic AI, Large Language Models, Machine Learning, Intelligent Document Processing, workflow orchestration and enterprise data within a single platform to automate complex finance and procurement processes. 

The important distinction is that Kanbina does not use Generative AI simply to answer questions or generate recommendations. AI is embedded directly within operational workflows so that it can understand a transaction, gather relevant information, determine what should happen next, take appropriate action and progress the process towards completion. 

This capability is already being applied within Kanbina across: 

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• Accounts Payable 

• Cash Allocations 

• Bank and Account Reconciliations 

• Procurement 

• Finance analytics and reporting 

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Within analytics, Kanbina's enterprise data model can be exposed through Microsoft Power BI, where Copilot provides Generative AI capabilities for interrogating data, identifying trends and performing analytical tasks through natural language. 

The result is a move from traditional finance automation towards intelligent, increasingly autonomous finance operations. 

 

What Agentic AI means in Kanbina 

A traditional AI assistant waits for a user to ask a question. 

An Agentic AI process works towards a business objective. 

For example, rather than simply answering: 

"Which invoices have not been matched?" 

Kanbina can undertake the process itself: 

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1. Identify the incoming transaction. 

2. Extract and understand the relevant information. 

3. Retrieve supplier, PO, receipt and accounting data from the ERP. 

4. Determine the appropriate matching and accounting treatment. 

5. Validate the result against business rules and previous behaviour. 

6. Identify whether an exception exists. 

7. Resolve or route the exception where appropriate. 

8. Progress the transaction towards approval or posting. 

9. Update the ERP or enterprise system. 

10. Record the outcome for control, reporting and audit. 

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This is the difference between AI providing intelligence and AI applying intelligence to perform work. 

Kanbina combines Agentic AI with its established finance automation platform, allowing intelligence to operate directly within controlled business processes rather than as a separate chatbot or advisory layer. 

 

A Hybrid AI Platform 

Kanbina does not believe every finance problem should be given to a single AI model. 

Different technologies are effective at different tasks, and Kanbina combines them within one orchestration platform. 

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Machine Learning 

Machine Learning is particularly effective at recognising patterns within high-volume financial data. 

Kanbina uses Machine Learning to identify relationships, predict coding, match transactions and learn from historic customer behaviour. 

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Large Language Models and Generative AI 

LLMs provide a different capability: the ability to understand language, context, instructions, policies and complex documents. 

Kanbina embeds enterprise LLM capabilities directly within finance and procurement workflows, allowing AI to interpret information that previously required human judgement. 

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Intelligent Document Processing 

Invoices, purchase orders, statements, shipping documentation, remittances and contracts continue to form a major part of finance operations. 

Kanbina converts these structured and unstructured documents into validated financial information that becomes part of the automated process. 

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Agentic AI 

Agentic AI brings these capabilities together. 

Once the relevant information has been gathered and understood, the AI can determine the next appropriate action and orchestrate the steps required to move the process towards completion. 

Enterprise Data and ERP Integration 

Agentic AI becomes materially more useful when it can interact with live enterprise data. 

Kanbina integrates with ERP, finance, procurement, banking and other enterprise systems so that AI can use real operational information and approved outcomes can become real business transactions. 

 

Agentic AI Across Kanbina 

Accounts Payable 

Agentic AI is already applied within Kanbina Accounts Payable. 

An incoming invoice can trigger an intelligent process in which Kanbina: 

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• Extracts invoice and supporting-document information. 

• Identifies the supplier. 

• Retrieves relevant ERP data. 

• Identifies Purchase Orders and receipts. 

• Determines likely coding and accounting treatment. 

• Matches the transaction. 

• Validates business and financial rules. 

• Investigates exceptions. 

• Progresses valid transactions towards approval and ERP posting. 

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This means the AP team does not need to investigate every invoice manually. The AI undertakes much of the investigation and only involves people when an exception, policy decision or higher-risk judgement genuinely requires their expertise. 

 

Cash Allocations 

Kanbina applies the same Agentic AI principles to Cash Allocation. 

An incoming receipt can be assessed against: 

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• Customer accounts 

• Open invoices 

• Payment references 

• Values and currencies 

• Historic payment behaviour 

• Remittance information 

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Rather than simply suggesting possible matches, the AI can gather the relevant information, determine the most likely allocation, validate the outcome and progress appropriate transactions towards posting. 

Finance users therefore supervise exceptions rather than manually researching every receipt. 

 

Reconciliations 

Agentic AI changes reconciliation from an exercise in identifying differences into a process for investigating and resolving them. 

Kanbina can bring together bank data, ledger transactions, historic reconciliations and supporting information to determine: 

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• Whether transactions match. 

• Why a difference exists. 

• Whether an allocation or accounting adjustment may be required. 

• Whether the transaction can be resolved automatically. 

• Whether an exception requires human investigation. 

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The objective is to reduce the amount of manual investigation required while maintaining financial control and auditability. 

 

Procurement 

Procurement is particularly suited to Agentic AI because purchasing decisions combine structured financial information with business context. 

Kanbina can bring together: 

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• User requirements 

• Approved suppliers 

• Previous purchasing history 

• Purchase Orders 

• Budgets 

• Accounting structures 

• Approval authorities 

• Supplier due diligence 

• Policies and controls 

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A user can describe what they need in natural language. The AI can interpret the requirement, identify purchasing options, recommend suppliers, determine coding, create a requisition, orchestrate approvals and progress the request towards Purchase Order creation. 

This allows procurement knowledge to be embedded within the AI rather than requiring every employee to understand purchasing policies and processes. 

 

AI-Powered Analytics 

Agentic AI does not stop when the transaction has been processed. 

Kanbina creates a rich enterprise data model containing operational information across finance and procurement processes. 

This data can be analysed through Microsoft Power BI, with Copilot providing Generative AI capabilities that allow users to interrogate information using natural language. 

Instead of relying entirely on predetermined reports, users can ask questions such as: 

"Which suppliers are generating the most AP exceptions?" 

"Which approval processes are causing the longest delays?" 

"Where has procurement spend increased most rapidly?" 

"Which customers have the highest level of unidentified cash?" 

The AI can help interrogate the underlying Kanbina data, identify patterns and support the automatic execution of analytical tasks. 

This creates an important closed loop: 

AI processes the transaction → Kanbina records the operational data → AI analyses performance → insight improves the process. 

 

From Copilot to Autopilot 

The direction of travel in enterprise AI is from assistance towards controlled autonomy. 

Kanbina supports this progression: 

Manual Processing 
People perform the process. 

↓ 

Rules-Based Automation 
Software performs predefined steps. 

↓ 

AI Assistance 
AI recommends what a person should do. 

↓ 

Intelligent Automation 
AI performs individual activities. 

↓ 

Agentic Automation 
AI works towards an objective, performs multiple connected activities and progresses the process towards completion. 

Kanbina is already operating across the later stages of this progression. 

The objective is not to remove people from finance and procurement. It is to remove the requirement for skilled people to spend their time performing repetitive investigation and administration. 

People remain responsible for genuine exceptions, material judgements and governance. 

 

AI That Can Act — With Governance 

Greater AI capability requires stronger governance. 

Kanbina Agentic AI operates within defined: 

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• Business rules 

• User permissions 

• Approval authorities 

• Confidence levels 

• Segregation of duties 

• Financial controls 

• Audit requirements 

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Depending upon the transaction and the organisation's policies, an AI-driven process can: 

Act automatically where confidence and controls permit straight-through processing. 

Recommend an action where human judgement or approval is required. 

Escalate an exception where further investigation is appropriate. 

Every action forms part of the governed Kanbina workflow and can be recorded for reporting, performance analysis and audit. 

Agentic AI therefore does not mean uncontrolled automation. It means giving AI increasing responsibility within clearly defined enterprise controls. 

 

One Agentic Platform Across Finance & Procurement 

The long-term opportunity is greater than automating individual tasks. 

Finance and procurement activities are interconnected: 

Procurement creates financial commitments. 

Accounts Payable processes the resulting liability. 

Payments affect cash. 

Cash Allocation affects customer balances. 

Bank transactions affect reconciliations. 

All transactions ultimately affect financial reporting and the close. 

Kanbina already operates across these processes within a common AI and orchestration platform. 

This provides the foundation for AI agents that can increasingly work across finance operations, rather than individual AI capabilities being confined to separate point solutions. 

 

What Makes Kanbina Agentic AI Different? 

Kanbina brings together capabilities that would otherwise require multiple technologies: 

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• Finance-specific AI 

• Designed around real transactional finance and procurement processes. 

• Machine Learning 

• Learns from financial patterns, transactions and historic behaviour. 

• Large Language Models 

• Understands language, documents, policies, instructions and business context. 

• Intelligent Document Processing 

• Transforms invoices and other unstructured documents into usable financial information. 

• Enterprise Data Orchestration 

• Combines information from ERP platforms, documents, email, banking and other enterprise systems. 

• Agentic Workflows 

• Determines and progresses the next appropriate actions towards a business outcome. 

• Transaction Execution 

• Moves beyond recommendations by allowing validated outcomes to become operational transactions. 

• AI-powered Analytics 

• Uses the Kanbina data model together with Power BI and Copilot to interrogate performance and automatically undertake analytical tasks. 

• Human Oversight 

• Keeps people focused on exceptions, judgement and higher-risk decisions. 

• Governance and Auditability 

• Provides visibility of AI decisions, workflow behaviour and automation performance. 

 

Agentic AI: The Next Stage of Finance Automation 

The first generation of finance automation digitised documents. 

The next automated workflows. 

Machine Learning introduced intelligent matching and prediction. 

Generative AI introduced language and contextual understanding. 

Agentic AI connects these technologies and enables AI to take responsibility for progressing an operational process towards an outcome. 

Kanbina is already applying that approach across Accounts Payable, Cash Allocations, Reconciliations and Procurement, while Power BI and Copilot extend AI into the analysis of the resulting enterprise data. 

That is the direction of the Kanbina platform: 

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• Not AI for the sake of AI. 

• Not another chatbot. 

• Not another dashboard showing finance teams what work needs doing. 

• AI that understands the work, helps perform the work and increasingly completes the work. 

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The biggest improvement here is that the document now presents a closed-loop Agentic AI architecture: operational AI performs transactions, Kanbina's data model captures what happened, and AI-enabled Power BI then analyses performance and feeds insight back into the operating process. That is a much stronger story than treating Agentic AI as only a workflow technology.